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Experimental and numerical investigation of cavity characteristics in behind-armor liquid-filled containers under shaped charge jet impact

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Defence technology 2025年 47卷 242-259页

作者： Ma, Shixin Li, Xiangdong Zhou, Lanwei School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China

The cavity characteristics in liquid-filled containers caused by high-velocity impacts represent an important area of research in hydrodynamic ram phenomena. The dynamic expansion of the cavity induces liquid pressure variations, potentially causing catastrophic damage to the container. Current studies mainly focus on non-deforming projectiles, such as fragments, with limited exploration of shaped charge jets. In this paper, a uniquely experimental system was designed to record cavity profiles in behind-armor liquid-filled containers subjected to shaped charge jet impacts. The impact process was then numerically reproduced using the explicit simulation program ANSYS LS-DYNA with the Structured Arbitrary Lagrangian-Eulerian (S-ALE) solver. The formation mechanism, along with the dimensional and shape evolution of the cavity was investigated. Additionally, the influence of the impact kinetic energy of the jet on the cavity characteristics was analyzed. The findings reveal that the cavity profile exhibits a conical shape, primarily driven by direct jet impact and inertial effects. The expansion rates of both cavity length and maximum radius increase with jet impact kinetic energy. When the impact kinetic energy is reduced to 28.2 kJ or below, the length-to-diameter ratio of the cavity ultimately stabilizes at approximately 7. © 2024 China Ordnance Society

关键词： Kinetic energy

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Improving UAV Image Target Detection: A Novel Approach Using OptiDETR with Swin Transformer

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 771-780页

作者： Ma, Wenlong Liu, Weisheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the analysis of drone aerial images, object detection tasks are particularly challenging, especially in the presence of complex terrain structures, extreme differences in target sizes, suboptimal shooting angles, and varying lighting conditions, all of which exacerbate the difficulty of recognition. In recent years, the DETR model based on the Transformer architecture has eliminated traditional post-processing steps such as NMS(Non-Maximum Suppression), thereby simplifying the object detection process and improving detection accuracy, which has garnered widespread attention in the academic community. However, DETR has limitations such as slow training convergence, difficulty in query optimization, and high computational costs, which hinder its application in practical fields. To address these issues, this paper proposes a new object detection model called OptiDETR. This model first employs a more efficient hybrid encoder to replace the traditional Transformer encoder. The new encoder significantly enhances feature processing capabilities through internal and cross-scale feature interaction and fusion logic. Secondly, an IoU (Intersection over Union) aware query selection mechanism is introduced. This mechanism adds IoU constraints during the training phase to provide higher-quality initial object queries for the decoder, significantly improving the decoding performance. Additionally, the OptiDETR model integrates SW-Block into the DETR decoder, leveraging the advantages of Swin Transformer in global context modeling and feature representation to further enhance the performance and efficiency of object detection. To tackle the problem of small object detection, this study innovatively employs the SAHI algorithm for data augmentation. Through a series of experiments, It achieved a significant performance improvement of more than two percentage points in the mAP (mean Average Precision) metric compared to current mainstream object detection models. Furthermore, ther

关键词： Decoding

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Enhancement of microwave absorption properties of Sr2+-doped CaMnO3 synthesized by Sol-Gel

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Ceramics International 2025年第13期51卷 17463-17472页

作者： Chang, Kaiheng Qian, Fengjiao Sun, Zhuqing Gu, Yuzhou Liu, Chuyang Yang, Hao MIIT Nanjing211106 China School of Materials Science and Engineering Nanjing University of Science and Technology Nanjing211106 China

CaMnO3 has garnered significant attention due to its remarkable thermoelectric properties. The high-temperature stability and dielectric characteristics of CaMnO3 could also underscore its potential as a microwave absorbing material. In this study, we reported the microwave absorption properties of CaMnO3 prepared by two different methods, solid-state reaction and sol-gel methods. The influence of the preparation method on the morphology and physical properties of CaMnO3 was studied. The results showed that the samples prepared by sol-gel method display much stronger reflection loss and wider absorption bandwidth. Based on the sol-gel method, the effect of Sr2+-doping on the absorption properties of CaMnO3 was also investigated. At Sr2+ doping concentration of 5 %, CaMnO3 displays remarkable reflection loss value of −45.96 dB accompanied with a broad effective absorption bandwidth of 5.04 GHz (8.5–13.5 GHz) covering the full X-wave band (8–12 GHz). Our results greatly extend the application frequency window of CaMnO3 from the high frequency Ku-wave band reported in the literature to the lower X-wave band, demonstrating CaMnO3 as an outstanding X-wave band absorber. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Sol-gel process

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TriCvT-DTI: Predicting Drug-Target Interactions Using Trimodal Representations and Convolutional Vision Transformers

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3451-3464页

作者： Maroua, Azouz Tian, Gang Wang, Rui Zhou, Jiehan Shandong University of Science and Technology Qingdao266510 China Shandong University of Science and Technology School of Computer Science and Engineering Qingdao266510 China Shandong University of Science and Technology College of Energy and Mining Engineering Qingdao266510 China Shandong University of Science and Technology Qingdao China Docent at University of Oulu Oulu Finland

Predicting interactions between drugs and their targets is vital for drug discovery and repositioning. Conventional techniques are slow and labor-intensive, while deep learning algorithms offer efficient solutions. However, deep learning often focus on single drug representations or simplistic combinations, leading to suboptimal feature representation. Moreover, the prevalent use of convolutional neural networks (CNNs) in drug image representation neglects the necessity for both local and global drug information in Drug-Target Interaction (DTI) tasks. To address these challenges, we propose TriCvT-DTI, a novel approach that combines molecular images, chemical sequence features, and graph representations of drugs to comprehensively capture structural, spatial, and functional aspects. TriCvT-DTI introduces a bidirectional multi-head attention mechanism for interactive feature learning between drugs and targets, enhancing performance by modeling complex relationships. By using Convolutional Vision Transformers (CvTs), TriCvT-DTI can effectively extract structural and spatial features from drug images. We evaluate our model on three datasets: Human, C. elegans, and Davis, and we compare it with state-of-the-art methods. Then we train TriCvT-DTI with uni-modality and bi-modality to compare then extract the impact of each modality on TriCvT-DTI. Experimental results demonstrate that TriCvT-DTI outperforms existing methods on both balanced and unbalanced datasets. Moreover, it presents impressive generalization capabilities on the Drug-Target Interaction (DTI) task. © 2013 IEEE.

关键词： Molecular imaging

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Evolutionary extreme learning machine based on an improved MOPSO algorithm

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Neural Computing and Applications 2025年第12期37卷 7733-7750页

作者： Ling, Qinghua Tan, Kaimin Wang, Yuyan Li, Zexu Liu, Wenkai School of Computer Science Jiangsu University of Science and Technology Zhenjiang212003 China

Extreme learning machine (ELM), as a single hidden layer feedforward neural network (SLFN), has attracted extensive attention because of its fast learning speed and high accuracy. However, the random selection of input weights and hidden biases is the main reason that deteriorates the generalization performance and stability of ELM network. In this study, an improved ELM (IMOPSO-ELM) is proposed to enhance the generalization performance and convergence stability of the SLFN by using a multi-objective particle swarm optimization (MOPSO) to determine the input parameters including input weights and hidden biases of the SLFN. Firstly, different from the traditional improved ELM based on single-objective evolutionary algorithm, the proposed algorithm used MOPSO to optimize the input weights and hidden biases of SLFN by considering the two objectives including accuracy on the validation set and the 2-norm of the SLFN output weights. Secondly, in order to improve the diversity and convergence of the solution set obtained by MOPSO, an improved MOPSO (IMOPSO) is proposed. The improved MOPSO uses a new optimal global particle selection strategy, by randomly dividing the population into several subpopulations, each subpopulation uses different particle information in the external archive to guide the subpopulation update, and uses the external archive set as the platform to share the information between sub-swarms. Finally, the experiment on the four regression problems and four classification problems verifies the effectiveness of the approach in improving ELM generalization performance and performance stability. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Extreme learning machine Generalization performance IMOPSO Performance stability

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PFHaD: Page Fault Handler Assisted Defense Against Data-Oriented Attacks 3rd

PFHaD: Page Fault Handler Assisted Defense Against Data-Orie...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Zhang, Tianning Huang, Hao School of Computer Jiangsu University of Science and Technology Zhenjiang China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783031876462

As memory corruption vulnerabilities evolve, attackers have shifted focus from traditional control-flow attacks to non-control data attacks, which manipulate data influencing a program′s behavior without altering its control flow. This shift bypasses conventional defenses like Control Flow Integrity (CFI), posing new security risks. In response, we propose a novel defense mechanism called Page Fault Handler-assisted Defense (PFHaD). PFHaD leverages hardware page faults to protect critical data in memory, providing robust defense against non-control data attacks with minimal performance overhead. Unlike traditional defenses, PFHaD also prevents information leakage by making critical data pages inaccessible. The solution does not require modifications to hardware, compilers, or operating systems, making it ideal for commercial-off-the-shelf (COTS) software. Experimental results demonstrate that PFHaD effectively mitigates a wide range of non-control data attacks while maintaining low performance impact, offering a scalable and efficient solution to modern memory corruption threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Program compilers

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UAV-assisted computing offloading and resource allocation based on Stackelberg game in vehicular edge computing

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International Journal of Vehicle Information and Communication Systems 2025年第1期10卷 20-42页

作者： Jiang, Yuhang Wu, Zhiyong Hu, Xiuwei Sun, Yilong Zheng, Yunhui School of Computer Science and Technology Shandong University of Technology Shandong Zibo China

Owing to the computational density and complexity of vehicle applications, unique vehicle mobility and limited edge server resources, Vehicle Edge Computing (VEC) faces significant challenges. Unmanned Aerial Vehicles (UAVs) are considered an important integrated part of extending the reach of edge computing and providing computing resources. In order to achieve efficient and reliable service and resource utilisation, we investigate UAV-assisted vehicle edge computational offloading and resource allocation mechanisms. The computing offload, migration and security models are analysed. A Stackelberg game model is constructed, iteratively analysing the interaction among the VEC servers, UAVs and VUs (VUs) to obtain an appropriate computational offloading and resource allocation scheme. Based on this, we propose an Improved Stackelberg Game Algorithm (ISGA) to optimise computational offloading and resource allocation. The simulation results show that the proposed algorithm is feasible and performs competitively compared to other methods. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： resource allocation Stackelberg game UAV vehicle edge computing

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Mobile robot path planning based on an improved ACO algorithm and path optimization

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Multimedia Tools and Applications 2025年第12期84卷 10899-10922页

作者： Zhou, Tianfeng Wei, Wenhong School of Computer Science and Technology Dongguan University of Technology Dongguan523808 China

This paper will improve the fundamental ant colony optimization algorithm in the context of mobile robot path planning in response to its flaws, which include easy descent into a local optimum, a large number of inflection points along the path, and an inefficient convergence speed. Based on the two-dimensional grid map modeling approach, map pheromone initialization is unevenly assigned to improve the blindness of the ant colony optimization algorithm in the initial phase of path planning. Furthermore, an adaptive pheromone volatility factor is proposed to improve the global characteristics of early path planning, accelerate the convergence speed as the iteration advances, and avoid a local optimum that could impede the algorithm's progress. The pheromone update mechanism was redesigned, and a reward and punishment mechanism was incorporated into the pheromone update function to accelerate the convergence of the algorithm. Finally, path optimization diminishes the quantity of inflection points along the path, which makes the path smoother and shortens the path length. The outcome of the experiments demonstrate that the improved ant colony optimization algorithm performs better, converges more quickly, and more closely matches the needs of actual mobile robot walking. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mobile robots

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Infrared small target detection based on density peaks searching and weighted multi-feature local difference

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Optoelectronics Letters 2025年第4期21卷 218-225页

作者： JI Bin FAN Pengxiang WANG Mengli LIU Yang XU Jiafeng School of Computer Science and Technology Anhui University of TechnologyMaanshan 243032China

To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-feature local ***,an improved high-boost filter is used for preprocessing to eliminate background clutter and high-brightness interference,thereby increasing the probability of capturing real targets in the density peak ***,a triple-layer window is used to extract features from the area surrounding candidate targets,addressing the uncertainty of small target *** calculating multi-feature local differences between the triple-layer windows,the problems of blurred target edges and low contrast are *** balance the contribution of different features,intra-class distance is used to calculate weights,achieving weighted fusion of multi-feature local differences to obtain the weighted multi-feature local differences of candidate *** real targets are then extracted using the interquartile *** on datasets such as SIRST and IRSTD-IK show that the proposed method is suitable for various complex types and demonstrates good robustness and detection performance.

关键词： extract featur background clutter density peaks searching infrared small target detection weighted multi feature local difference capturing real targets density peak infrared small target detectionthis

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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